Adaptive radial basis functions

Proceedings - International Conference on Pattern Recognition

Volumn 4, Issue , 1996, Pages 630-634

  Webb, Andrew R ^a   Shannon, Simon ^a  

^a DEFENCE SCIENCE AND TECHNOLOGY LABORATORY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

OPTIMIZATION; PATTERN RECOGNITION; RADIAL BASIS FUNCTION NETWORKS; REGRESSION ANALYSIS;

BASIS FUNCTIONS; FUNCTIONAL FORMS; HIGH DIMENSIONAL DATA; NON-LINEAR MODEL; PROJECTION PURSUITS; RADIAL BASIS FUNCTIONS;

FUNCTIONS;

EID: 0039198970     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPR.1996.547641     Document Type: Conference Paper

References (17)

1. L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Wadsworth International Group, Belmont, California, 1984.
2. L. Breiman and R. Ihaka. Nonlinear discriminant analysis via scaling and ace. Technical report number 40, Department of Statistics, University of California, Berkeley, 1984.
3. D. Broomhead and D. Lowe. Multi-variable functional interpolation and adaptive networks. Complex Systems, 2(3):269-303, 1988.
4. J. Friedman. Multivariate adaptive regression splines. The Annals of Statistics, 19(1):1-141: 1991.
5. G. Golub, M. Heath, and G. Wahba. Generalised cross-validation as a method for choosing a good ridge parameter. Technometrics, 21:215-223, 1979.
6. P. Green and B. Silverman. Nonlinear regression and generalized linear models. A roughness penalty approach. Chapman and Hall, London, 1994.
7. D. Hand. Kernel Discriminant Anulysis. Pattern Recognition and Image Processing Research Studies Series. Research Studies Press, Letchworth, Herts, UK., 1982.
8. T. Hastie, R. Tibshirani, and A. Buja. Flexible discriminant analysis by optimal scoring. Journal of the American Statistical Association, 89(428):1255-1270, 1994.
9. P. Huber. Projection pursuit (with discussion). The Annals of Statistics, 13(2):435-52, 1985.
10. M. Jones and R. Sibson. What is projection pursuit (with discussion). Journal of the Royal Statistical Society A, 150:1-36, 1987.
11. D. Lowe. On the use of nonlocal and non positive definite basis functions in radial basis function networks. In IEE International Conference on Artificial Neural Networks, 1995.
12. D. Lowe. Radial basis functions. In M. Arbib, editor, The Handbook of Brain Theory and Neural Networks. Bradford Books/MIT Press, 1995.
13. G. Peterson and H. Barney. Control methods used in a study of vowels. Journal of the Acoustical Society of America, 24:175-184, 1952.
14. D. Rumelhart, G. Hinton, and R. Williams. Learning internal representation by error propagation. In Parallel Distributed Processing: Explorations in the Microstructure of Cognition. MIT Press, Cambridge, MA, 1986.
15. D. Specht. Probabilistic neural networks. Neural Networks, 3 (1) : 109-118, 1990.
16. A. Webb. Functional approximation in feed-forward networks : A least-squares approach to generalisation. IEEE Transactions on Neural Networks, 5(3):363-371, 1994.
17. A. Webb and S. Shannon. Spherically-symmetric basis functions for discrimination and regression: determining the nonlinearity. Technical Report DRA/CIS(SE1)/651/2/JP95/22/2.0., DRA, St Andrews Road, Malvern, Worcs, UK, 1996.